Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	137,490
2	South Dakota	135,312
3	Rhode Island	133,190
4	Utah	121,699
5	Tennessee	118,324
6	Arizona	116,718
7	Iowa	113,157
8	Oklahoma	111,905
9	Wisconsin	110,897
10	Nebraska	110,795
11	Arkansas	110,064
12	South Carolina	109,287
13	New Jersey	107,348
14	Alabama	105,828
15	Kansas	105,212
16	Indiana	104,272
17	Mississippi	103,304
18	Idaho	102,546
19	Illinois	101,256
20	Delaware	101,225
21	New York	100,485
22	Nevada	100,004
23	Montana	99,394
24	Florida	98,903
25	Georgia	98,890
26	Wyoming	98,267
27	Kentucky	97,678
28	Texas	97,502
29	Louisiana	96,549
30	Missouri	96,453
31	Minnesota	96,128
32	Massachusetts	95,693
33	California	93,658
34	New Mexico	92,219
35	Connecticut	90,199
36	North Carolina	89,173
37	Ohio	88,925
38	Alaska	87,840
39	Pennsylvania	84,148
40	Colorado	83,582
41	Michigan	82,105
42	West Virginia	81,560
43	Virginia	74,459
44	Maryland	70,615
45	New Hampshire	65,347
46	District of Columbia	64,938
47	Washington	49,646
48	Puerto Rico	46,627
49	Oregon	40,440
50	Maine	40,142
51	Vermont	33,978
52	Hawaii	21,926

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	541
2	New Jersey	419
3	Minnesota	406
4	Delaware	399
5	New York	388
6	Puerto Rico	369
7	New Hampshire	346
8	Pennsylvania	330
9	Massachusetts	319
10	Florida	303
11	Vermont	284
12	Colorado	272
13	Maryland	270
14	Illinois	267
15	West Virginia	250
16	Maine	239
17	South Carolina	200
18	South Dakota	191
19	Indiana	190
20	Nevada	184
21	Rhode Island	177
22	Virginia	174
23	Connecticut	168
24	North Dakota	166
25	Ohio	166
26	Iowa	165
27	District of Columbia	156
28	Wisconsin	156
29	Nebraska	153
30	Oregon	140
31	Georgia	126
32	Utah	124
33	Arizona	121
34	Washington	116
35	Alaska	112
36	Kentucky	111
37	Montana	108
38	Missouri	100
39	California	88
40	Hawaii	87
41	Texas	81
42	North Carolina	75
43	Alabama	66
44	Mississippi	66
45	Tennessee	63
46	Idaho	57
47	Louisiana	52
48	Arkansas	49
49	Kansas	47
50	Wyoming	40
51	Oklahoma	35
52	New Mexico	19

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,799
2	New York	2,601
3	Massachusetts	2,522
4	Rhode Island	2,490
5	Mississippi	2,384
6	Arizona	2,347
7	Connecticut	2,228
8	South Dakota	2,199
9	Louisiana	2,197
10	Alabama	2,184
11	Pennsylvania	1,988
12	North Dakota	1,970
13	Indiana	1,952
14	New Mexico	1,897
15	Illinois	1,877
16	Arkansas	1,875
17	Iowa	1,856
18	South Carolina	1,801
19	Georgia	1,778
20	Michigan	1,757
21	Tennessee	1,742
22	Nevada	1,731
23	Texas	1,702
24	Kansas	1,694
25	Oklahoma	1,685
26	Delaware	1,620
27	Ohio	1,610
28	Florida	1,583
29	District of Columbia	1,531
30	West Virginia	1,531
31	California	1,529
32	Missouri	1,467
33	Montana	1,424
34	Kentucky	1,421
35	Maryland	1,398
36	Wisconsin	1,267
37	Minnesota	1,247
38	Virginia	1,226
39	Wyoming	1,211
40	Nebraska	1,204
41	North Carolina	1,170
42	Idaho	1,116
43	Colorado	1,078
44	New Hampshire	924
45	Washington	705
46	Puerto Rico	674
47	Utah	673
48	Oregon	581
49	Maine	557
50	Alaska	408
51	Vermont	370
52	Hawaii	330

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Montana	8
2	Delaware	3
3	Georgia	3
4	Michigan	3
5	Nebraska	3
6	New Jersey	3
7	New York	3
8	Alabama	2
9	California	2
10	Kentucky	2
11	Maryland	2
12	Minnesota	2
13	Nevada	2
14	Ohio	2
15	Pennsylvania	2
16	South Carolina	2
17	South Dakota	2
18	Texas	2
19	Arizona	1
20	District of Columbia	1
21	Florida	1
22	Illinois	1
23	Indiana	1
24	Iowa	1
25	Louisiana	1
26	Massachusetts	1
27	Mississippi	1
28	New Hampshire	1
29	North Dakota	1
30	Utah	1
31	Virginia	1
32	West Virginia	1
33	Alaska	0
34	Arkansas	0
35	Colorado	0
36	Connecticut	0
37	Hawaii	0
38	Idaho	0
39	Kansas	0
40	Maine	0
41	Missouri	0
42	New Mexico	0
43	North Carolina	0
44	Oklahoma	0
45	Oregon	0
46	Puerto Rico	0
47	Rhode Island	0
48	Tennessee	0
49	Vermont	0
50	Washington	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	355,222	1	99
Chattahoochee	Georgia	341,340	2	99
Bent	Colorado	266,631	3	99
Dewey	South Dakota	244,908	4	99
Lincoln	Arkansas	244,702	5	99
Davidson	Tennessee	137,797	194	93
Richland	South Carolina	107,428	1013	67
York	South Carolina	104,342	1150	63
Orange	California	84,446	2081	33
Pierce	Washington	49,234	2896	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Gove	Kansas	8,346	2	99
Jerauld	South Dakota	7,948	3	99
Galax city	Virginia	7,878	4	99
Emporia city	Virginia	7,856	5	99
Orange	California	1,524	1780	43
Davidson	Tennessee	1,322	2029	35
York	South Carolina	1,313	2038	35
Richland	South Carolina	1,287	2070	34
Pierce	Washington	686	2726	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.